perm filename PAPERA.1[DOC,TOB]1 blob
sn#022986 filedate 1973-02-20 generic text, type T, neo UTF8
00100 applications of robotics research
00200 automated assembly
00300 computerization of industry
00400 group technology
00500 scheduling and routing
00600
00700
00800
00900
01000 benefits of robotics research
01100 productivity increases
01200 social need for productivity
01300 social service needs
01400 more leisure
01500 unmet material needs: housing,public transit, poverty
01600
01700 areas of robotics most likely to have short term payoff:
01800 manipulation w/ touch
01900 representation of shape and space
02000 perceptual systems-integrated systems
02100
02200 necessity for programmibility in robotics
02300
02400 structure of models
02500
02600 knowability of things
02700 permanence
02800 contiguity
02900 computational requirements
03000 separability of objects
03100 independence of variables
03200 interactions separately described
03300 classification:objects cluster can be classified into similarity classes
03400 structure part/whole
03500 form=function
03600
03700 mechanistic interpretation of affective behavior
03800 eg force someone to do something
03900 symbolic models
04000
04100
04200 information science in production
04300 systematizing control of production
04400 modularization and commonality of parts and operations
04500 description of parts and objects
04600 design automation
04700 analysis of tasks
04800 communication and file storage
04900 scheduling
05000 routing
05100 sensing, monitoring
00100 REPRESENTATION OF SHAPE
00200
00300 what tasks required of a representation
00400 choice of parts
00500 manipulation
00600 avoidance
00700 display
00800 recognition
00900 visual feedback
01000 perceptual strategies
01100 learning
01200 3-d cues
01300
01400 what operations required
01500 display-2 dim proximity
01600 manipulation-center of gravity,center of rotation,length, thickness
01700 avoidance-3 dim proximity, find free space
01800 visual memory-encode shape into memory reference descriptors
01900 recognition-features,visual memory,hypotheses,prediction,verification
02000 geometric relations among parts
02100
02200 simplicity criteria for cross section function
02300 constant
02400 monotonic
02500 continuous
02600 number of maxima
02700
02800 curve representation is topologically inadequate in 2d
02900 surface representation is topologically inadequate in 3d
03000 a)curve is one-dimensional
03100 surface is two-dimensional
03200 b)when do we need higher dimensions-proximity
03300 choice of parts
03400 volume-parts vs surface-parts
03500 area-parts vs curve-parts
03600 c)local realizability
03700 d)dimension arguments
03800
03900 proximity
04000
04100 fabrication operations
04200 form=function heuristics
04300 ease man-machine communication technology transfer
04400 input devices -3d
04500 symbolic input
04600 high-level manipulation
04700 predictive results for perception
04800 initial organization
04900
05000 level of representation
05100 symbolic descriptions--iconic descriptions
05200 example of gravity discussed at coffee
05300 what are we going to do with descriptions
05400 descriptions relative to what we are going to do with them
05500
05600 important properties are not in 2d image: 3d interpretation
05700
05800 system should be a programming language
05900 modular
06000 heterarchical
06100 redundancy, consistency, inconsistency
06200 problem-solving system
06300 approximation of decision trees by statistical estimates
06400
06500 desiderata for representation
06600 generative
06700 levels of detail
06800 3d interpretation
06900 high level
07000 mechanism for similarity
07100 part/whole segmentation structured description
07200
07300 schema, additions, deletions, transformations
07400
07500 volume representation
07600 similarity as a paradigm problem
07700 choice of paradigms
07800 principle of redundancy, hypothesis and verification
07900
08000 significance of shape representation
08100 fabrication operations
08200 form=function
08300 basis for extending Winston's learning paradigm
08400 ease of man-machine communication
08500 input 3d
08600 display
08700 primitives for programming
08800 common vocabulary
08900 useful in initial organization stages
09000 useful for monocular vision
09100
09200 group technology and overall computer control of production
09300 analysis of assembly tasks
09400 description of parts and objects
09500 modularization of process
09600 commonality
09700 automated parts description
09800 task programming for robots
09900 machining descriptions,selective accuracy and monitoring
10000 graphics
10100 visual feedback
10200
10300
10400 partial knowledge--backs of objects invisible in single scene
10500
10600 visual memory
10700
10800 3d representation valuable since accurate predictions can always
10900 be made; not true with 2d representation
11000
11100 for man-machine communication, machine should have same
11200 concepts as man
11300 understands objects in terms of pieces joined
11400 or in terms of fabrication operations (lathe,drill)
11500 pasting
11600 relationships
11700 near
11800 touch
11900 inside
12000 join
12100 cross
12200 overlap
12300 parallel
12400 corresponding
12500 adjacent
12600 above
12700 left,right,in front,behind
12800
12900 see many problems as AI problems
13000 graphics
13100 computer languages
13200
13300 experimentation and learning
13400 three models for learning
13500 statistical inference
13600 deductive/inductive
13700 experimental paradigm of construction of models
13800 independence of variables, specified interactions
13900
14000 depth information as a way of building up the map
14100
14200 bilateral symmetry
14300
14400 three-dimensional cues
14500
14600 joints
14700 sleeves
14800 parallel joints-flat
14900 conservation at joints
15000 intersections as cues to shape
15100 depth measurement techniques
15200 laser
15300 stereo
15400 phase modulation
15500 time of flight
00100 description
00200 computational aspects
00100 Many of us now believe that robotics will ultimately transform much
00200 of industry; we cannot estimate how rapidly this change will happen,
00300 but we expect a beginning within a decade, and considerable effect
00400 over a few decades. Initially, touch, simple vision and computer
00500 control can incrementally improve industrial manipulators which now
00600 have no sensing and no contingency actions. In the industrial
00700 context, visual feedback functions in constrained situations, with
00800 known objects and background , and approximately known position and
00900 orientation, for alignment of screws with holes, mating surfaces,
01000 etc. Not only is this problem area of technological importance, but
01100 visual feedback is a useful paradigm for research. Current efforts
01200 in artificial intelligence concentrate on goal-oriented visual
01300 systems which take advantage of knowledge of the environment.
01400
01500 For robots to be highly effective in assembly and other industrial
01600 tasks, their programming for new tasks must be made simple and rapid.
01700 Of course, even if programming is difficult, there will be many
01800 applications for which it is useful to stockpile programs of
01900 production operations rather than stockpile parts. But for ultimate
02000 impact, the programming of assembly devices is of great importance. A
02100 mini-computer to control an assembly device is adequate for
02200 manipulation and simple visual operations. Programming tasks for the
02300 assembly robot can be done on a central time-sharing computer
02400 facility with large memory, file storage, advanced languages and
02500 graphics. That system could be an internal system for scheduling and
02600 production control, or an external service. For this primary
02700 problem in robotics, the whole of AI research is available for
02800 application, and the most sophisticated robotics research is
02900 relevant.
03000
03100 But on a broader scale, the computerization of industry will affect
03200 production on a shorter time scale and equally profoundly. The
03300 problems under consideration in AI and the representations of shape
03400 and space which are evolving, have great relevance to the impact of
03500 information science on production. The most far-reaching
03600 consequences will come from intellectual economies of scale which
03700 follow from systematizing production control, from modularization and
03800 commonality of parts and operations, typified by the "group
03900 technology" approach of Opits. In this analysis, parts are coded by
04000 descriptors; facilities are reorganized to produce similar elements
04100 in the same facilities. More generally, the availability of design
04200 records makes possible identification of common parts and processes,
04300 and possibility for redesign. These abilities rest upon facilities
04400 for design automation, description of parts and objects,
04500 communication and filing, scheduling, routing, and sensing and
04600 monitoring of production. Implicit in all this is the ability to
04700 represent objects and operations symbolically.
04800
04900 Minsky and Papert [Minsky 1972] have described the central issues and
05000 the relevance of robotics in artificial intelligence. These issues
05100 in intelligence are emphasized:
05200 symbolic descriptions of facts and computational processes
05300 manipulation of descriptions
05400 heterarchical control structures--problem-solving controlled
05500 by heuristics which depend on the meanings of events.
05600 Our concern is implementation of symbolic descriptions of shape and
05700 space which are adequate for integrated robot systems. We do not
05800 think a single representation is adequate throughout a system, but we
05900 have attempted to analyze tasks in terms of computational primitives
06000 (originating from topological and geometrical primitives as in the
06100 computational geometry scheme of Minsky and Papert [Minsky 1969]) and
06200 design representation abilities accordingly. Representative tasks
06300 are manipulation, avoidance, display, and recognition. Manipulation
06400 requires mass and moments of articulated parts, and ability to
06500 calculate overall moments in various positions and orientations.
06600 Collision avoidance in manipulation requires three-dimensional
06700 proximity, to find objects near a trajectory or near an object to be
06800 grasped. Display requires description of surface properties and two
06900 dimensional proximity, to find overlapping surfaces in the image.
07000 Recognition requires visual memory for generation of hypotheses,
07100 prediction and verification facilities, geometric relations among
07200 parts, and a good choice of parts.
07300
07400 We haven't space for a survey of all research in integrated visual
07500 systems, nor to discuss modules for three-dimensional interpretation
07600 of line drawings, edge and region description, corner finding,
07700 prediction, etc. We will concentrate on relating progress in
07800 description of complex objects and its relevance to complete visual
07900 systems. Figures 1 through 3 show experimental results for computer
08000 description of some objects, from a program by Agin at Stanford
08100 [Agin]. The program creates intuitively natural descriptions of
08200 parts for objects such as those shown. The description system
08300 depends on representations designed by te author, and on a laser
08400 triangulation system described by the author and implemented by Agin.
08500
08600 Although many would disagree about criteria for representation, and
08700 although other types of representations have various uses in an
08800 integrated system, the desiderata for representations were:
08900
09000 a) Level of Representation
09100 Representations should be chosen at a symbolic level resembling
09200 intuitive descriptions in order to interface with heuristics of the
09300 problem domain. Low level representations should be generated from
09400 the high level representation, or referenced as special cases from
09500 the high level representation.
09600
09700 b) Segmentation
09800 Representations should depend on segmentation of an object into
09900 parts. The parts themselves may be segmented into subparts to allow
10000 levels of detail. The value of this segmentation depends entirely on
10100 the choice of primitives and the segmentation procedures.
10200
10300 c) Primitives
10305 Primitives should be three-dimensional and body-centered.
10310 Translational and rotational invariance are possible as a
10315 consequence. Representations should have volume primitives. Assume
10320 that we mean by a primitive something which is represented by a
10325 description continuous in a natural sense given by the
10330 representation. Then surface primitives do not correspond to the
10335 parts we conveniently think of when we segment an object. A surface
10340 primitive would be a continuous surface, eg the soles of our feet,
10345 instead of the feet themselves. Rather, we think in terms of
10350 arm-like pieces. Intuitively, it seems natural that representation
10355 primitives have the same dimension as the structure represented,
10360 allowing a finite covering, not a lower dimension requiring a
10365 non-denumerable covering. To correspond to objects, representations
10370 must be realizable, that is, closed and non-intersecting. The
10375 primitives we have proposed are locally realizable, that is, the
10380 volume elements are closed and non-intersecting. Globally, arms may
10385 intersect, however. Surface representations, are not locally
10390 realizable, that is, an infinitesimal element is not closed; only
10395 when we have global information can we test whether the surface is
10400 closed.
12200
12300 d) Generative descriptions
12305 Representations should be generative.
12400 That is, local primitives should allow a very rich set of shapes to
12500 be conveniently generated.
12600
12700 THE REPRESENTATION OF SHAPE
12800
12900 The representations [Binford 1971] depend upon segmentation to
13000 describe complex objects in terms of parts. The representations are
13100 graph-structured: nodes correspond to parts, while arcs correspond to
13200 relations between parts. Parts are themselves structures of the
13300 same form. The relations include relative position and orientation,
13400 the degrees of freedom, symmetry, and any detailed knowledge of joint
13500 structure. Examples are neck joint, shoulder joint, and hip joint of
13600 a human. Segmentation and graph-structured representation are not
13700 unique; however, their value depends on an explicit elaboration of
13800 primitives which lead to a useful choice of parts.
13900
14000 The topological operations of cutting and pasting are used in joining
14100 parts. Normally, we think of holes as made by cuts and
14200 protuberances as made by pasting; this can be done purely formally,
14300 but we would like to generalize that to a choice between the simpler
14400 of structures, with cuts or glue. In fabrication, cutting is often
14500 used to form even complicated protuberances; attaching seems more
14600 difficult, particularly when dimensional accuracy is necessary. The
14700 representation reflects some fabrication operations, and allows
14800 embedding of heuristic knowledge. The representations resemble
14900 construction algorithms. But we would not expect that reasonable
15000 construction algorithms could emerge until a micro-world of
15100 fabrication operations is available for evaluation of simplicity.
15200
15300 We have concentrated on the inverse problem of building up these
15400 structures from visual data. When possible, the choice of parts and
15500 description of joint relations uses whatever knowledge is available.
15600 When guesses are necessary, our heuristics correspond to our
15700 intuition about shape.
15800
15900 The primitive parts are described by volume representations. We
16000 maintain that a useful choice of parts is in terms of arm-like parts,
16100 which come naturally from the representation. The primitive parts are
16200 described by "generalized local cones". These are the volumes swept
16300 out by taking an arbitrary cross section and translating it along a
16400 path, maintaining the cross section normal to the path, while the
16500 scale of the cross section is changed smoothly. More generally, we
16600 describe shapes generated from arbitrary cross sections by
16700 infinitesimal transformations including translations compounded with
16800 scaling, rotations, and shape changing operations applied to the
16900 cross sections. The basis of this representation is the description
17000 of a large class of shapes based on transformation of local
17100 descriptions. The typical element is a snake-like piece, described
17200 by an axis (which is a space curve) and normal cross sections. The
17300 cross sections are arbitrary. Local cylinders, cones, toroids, and
17400 helices, all with arbitrary cross sections, are typical examples.
17500
17600 The process of segmentation allows non-unique representations. This
17700 permits a choice of simple representations; we can regard it as an
17800 advantage that we have such a choice. We have a simplicity criterion
17900 of shapes based on the transformation function on the cross section
18000 (constant=cylinder, linear=cone, monotonic, convex, single maximum,
18100 shape changing...). As an example:
18200
18300
18400
18500 MAN
18600
18700 [HEAD]
18800 |
18900 NECK
19000 |
19100 [ARM]--<SHOULDERS>--[ARM]
19200 |
19300 TRUNK
19400 |
19500 <HIPS>
19600 | |
19700 [LEG] [LEG]
19800
19900
20000
20100
20200
20300 where brackets indicate complex parts and <> indicate joints.
20400 ARM is: ARM
20500
20600 UPPER ARM
20700 |
20800 <ELBOW>
20900 |
21000 FOREARM
21100 |
21200 <WRIST>
21300 |
21400 <HAND>
21500 | | | | |
21600 [FINGER] [FINGER] [FINGER] [FINGER] [THUMB]
21700
21800
21900
22000 A primitive has the representation:
22100
22200 FOREARM
22300 | | |
22400 CROSS AXIS TRANSFORMATION
22500 SECTION
22600
22700 The primary considerations for the utility of the representation
22800 concern the tasks we will need to perform. These tasks can be
22900 described in various ways; the most enlightening description of tasks
23000 is in terms of their computational structure. For these problems,
23100 topological and geometric considerations are important. Surface
23200 representations have been used for display purposes. For robotics
23300 applications, the representation for manipulation would need to make
23400 explicit how to pick up an object, i.e. where is the center of mass,
23500 the center of rotation, and what are the moments. How thick is the
23600 piece, and how long? These are more than just surface properties,
23700 and the surface representation is no help in determining these
23800 properties. These properties are the sort of properties which are
23900 natural for our volume representations; however surface properties
24000 are quite naturally obtained in this representation.
24100
24200 The cross sections are represented in the same fashion in two
24300 dimensions as objects are in three dimensions. The part/whole
24400 segmentation is crucial. The parts which are useful are not just
24500 curves, but arm-like areas. Thus we are interested in area
24600 representations, not curve representations. The descriptions in terms
24700 of a two-dimensional axis and normal width are similar to the volume
24800 representations described by a space curve and normal cross section.
24900
25000 CROSS SECTION
25100 [AREA-PART]--<JOINT>--[AREA-PART]
25200
25300
25400 where a part of a cross section is an area defined by subparts. A
25500 primitive area-part is defined by:
25600 PRIMITIVE AREA-PART
25700 | | | |
25800 WIDTH AXIS TRANS SURFACE
25900 PROPERTIES
26000
26100
26200 The coordinate system is local to each element. Each link connects
26300 two joints. Each link contains a transformation required to go from
26400 the coordinate system of an element at one joint, to the coordinate
26500 system of an element at the other joint. This is a translation
26600 combined with a rotation. It is most convenient to define the local
26700 coordinates in terms of actual vectors rather than with an arbitrary
26800 basis.
26900
27000 DEPTH MEASUREMENT TECHNIQUES
27100
27200 The human visual system uses both stereo and motion parallax to gain
27300 depth information. The two function in approximately the same way.
27400 With stereo, the images seen by the two eyes are nearly alike. With
27500 motion parallax, successive images are nearly alike. Thus, to
27600 correspond a point in one image with a point in the other, a search
27700 over a small range is necessary. With motion parallax, corresponding
27800 points can be tracked through successive images to achieve a large
27900 baseline, and depth resolution at large distances. Relative depth
28000 information (ie one point nearer by two inches) requires only the
28100 shift between correspondences. Motion parallax was used by Nevatia
28200 [Nevatia] to measure depth of points on samples of rocks. With a 50
28300 mm lens and a 5 degree baseline at a meter, depth accuracy was about
28400 2 mm. This was measured for a cup with circular cross section. That
28500 correspondence error is about 1/8 th tv picture cell. The
28600 corresponding phenomenon is noted in human vision in stereo and
28700 vernier acuity. It has a straightforward explanation in terms of
28800 expected errors based on the correlation process.
28900
29000 Work is underway at several laboratories on stereo correlation. [Quam
29100 1972]. Current programs are inadequate conceptually at depth
29200 discontinuities and with respect to ambiguities; they require about
29300 20 minutes with PDP-10 class machines. There are major improvements
29400 which can be made in the strategies for correspondence, and factors
29500 of approximately 100 in speed can be gained by low cost special
29600 purpose visual processors.
29700
29800 Laser depth ranging hardware has been operating at Stanford since
29900 January, 1971[Agin]. As in stereo, we know the angles of the rays
30000 from the laser to the object, and from the object to the camera. Thus
30100 we know the baseline and two angles of a triangle, which completely
30200 determines the position of the object illuminated by the laser. As
30300 shown in figure 4, the laser beam is diverged into a plane, to
30400 simplify the deflection sytem to one dimension, and speed data taking
30500 without introducing ambiguity. Each ray from the laser appears at a
30600 unique angle in the video image. The approximate depth resolution is
30700 about 1 mm at 1 meter. The limit on the speed of reading is about 20
30800 scans per second, with about 100 scans required for a reasonable
30900 resolution. That scan time is shorter than the corresponding
31000 computation time. The laser required to operate with a silicon
31100 vidicon tube costs only about $250.
31200
31300 Other techniques are described in a sensor study [Binford 1972].
31400 Ultrasonics have too slow a sample rate. Most other techniques are a
31500 form of triangulation as in the laser system just described, or
31600 measure the time of flight or phase change of modulated signals over
31700 the flight time. Since light travels at 1 ft per nanosecond, the
31800 measurement of 1 mm resolution requires 2 picosecond time resolution.
31900 Presently, most optical depth-measuring techniques have speed and
32000 resolution more appropriate for surveying or vehicle navigation than
32100 computer-controlled assembly, but techniques exist for substantial
32200 improvement.
32300
32400 TECHNIQUES OF DESCRIPTION
32500
32600 We describe briefly some of the work from Agin's thesis [Agin]. The
32700 plane of the laser beam intersects objects along space curves which
32800 project as curves from the viewpoint of the camera. These are shown
32900 in figure 5. Depth discontinuities in space appear as breaks in
33000 the curve in the image and slope discontinuities in space appear as
33100 slope discontinuities in the image. In the representation, parts are
33200 defined by continuity of axis and normal cross section. The path
33300 through corresponding points (midpoints) of cross sections should be
33400 everywhere normal to the cross sections. That calculation is stable
33500 for successive approximation. Some spurious local axes are removed by
33600 extending local axis descriptions to impose continuity. There
33700 remain some parts which are described in several ways. These
33800 alternate descriptions must be evaluated by a more global choice.
33900
34000 To find good starting points for local axis descriptions, portions of
34100 objects are found for which the scan cross sections (usually not
34200 normal to the eventual axis) are smoothly changing and overlap. An
34300 estimate of the axis for the group is made by connecting the
34400 midpoints of scan cross sections. The midpoints are a good
34500 approximation to the axis even when the scan is relatively far from
34600 normal to the axis. Axis estimates from preliminary groupings for a
34700 doll are shown in figure 6. The approximate axis gives an estimate
34800 for the directions of normal cross sections, which in turn give an
34900 improved axis estimate. The process quickly gives an axis determined
35000 by normal cross sections. The pieces are thus described as local
35100 "generalized cones". An estimate is made of the cross section
35200 function, and the cone is extended by another iterative description
35300 process. Eventually, when the description is extended as far as
35400 possible, it is broken off. The condition for breaking off is that
35500 the cross section function must fit a parabola well over the whole of
35600 the axis. Figures 1 through 3 show the descriptions of parts for
35700 some objects. Because the program does not have good representations
35800 of the boundaries, the crude tests for cross sections crossing
35900 boundaries are sometimes inadequate, and two parts may be linked
36000 together, causing some gross errors in description of parts. These
36100 operations are performed in three-dimensions, and circular cross
36200 sections are assumed. The assumpton of circular cross sections is
36300 only an expedient and not at all fundamental to the method.
36400
36500 For objects consisting of one part, such as a ring or snake, the
36600 descriptions are quite natural. A ring is described as an element of
36700 constant cross section along a closed, circular axis. More
36800 complicated objects with several parts have reasonable descriptions
36900 of the parts, but the parts are not connected together, so that there
37000 is no sense of complete structure.
37100
37200 CURRENT DIRECTIONS
37300
37400 The description techniques have exemplified the idea of a
37500 computational equivalent to a representation. Parts are
37600 characterized by continuous axis direction and continuous cross
37700 sections. In generalizing the class of cross sections, another
37800 direct computational realization emerged: the realization of the
37900 representation in terms of "generalized translational invariance"
38000 [Binford].
38100
38200 The descriptions are intended for recognition, among other things.
38300 To that end, the current work is involved in forming complete
38400 descriptions of objects, linking parts together at joints, choosing
38500 among alternative descriptions. The paradigm is recognition from the
38600 system's own descriptions among a potentially very large visual
38700 memory. The techniques of visual memory are closely coupled to these
38800 symbolic descriptions and will be described in forthcoming work.
38900
39000
39100 We have discussed representation and description without mentioning
39200 statistical learning. We think that structured learning is more
39300 relevant, and that the best examples of learning schema have been
39400 Winston's [Winston]. Instead of many trial learning, that system
39500 learned significant new structures on a single trial learning. But
39600 the essential difference from statistical schemes is that the
39700 learning consisted in building up symbolic descriptions, manipulating
39800 them and comparing them. Instead of examples contributing to some
39900 diffuse evaluation function, comparison of an example description
40000 with the concept description causes modification of the concept
40100 description specific to the differences. What types of descriptions
40200 are useful? Winston's work was successful because his primitives
40300 were geometric relations like left/right, above/below,
40400 supporting/supported-by, etc, and he was able to express interesting
40500 concepts of geometric structures as simple combinations of these
40600 geometric relations. We aim for an extension of these ideas with an
40700 understanding of form and its semantics, as expressed in a richer
40800 description of form and relations. The ability to make descriptions
40900 and modify them enables the system to remember objects. There is no
41000 description of procedures and ability to modify such descriptions;
41100 consequently, the system cannot learn about how to perceive complex
41200 objects.
00100 [Anderson 1972] R. Anderson,"Programmable Automation, The Future
00200 of Computers in Manufacturing", Datamation, Dec 1972.
00300
00400 [Agin] G. Agin, "Description and Representation of Curved Objects",
00500 Ph.D. Thesis, Computer Science Dept., Stanford University, September
00600 1972.
00700
00800
00900 [Binford 1972]"Sensor Systems for Manipulation", Conference on Remotely Manned
01000 Systems, JPL, Pasadena, Calif, Sept 1972.
01100
01200 [Binford 1971] "Visual Perception by Computer", Invited paper at IEEE Systems
01300 Science and Cybernetics, Miami, Dec 1971.
01400
01500
01600
01700 [Minsky 1972] "Artificial Intelligence Progress Report", AI Memo 252,
01800 MIT AI Lab, (1972);
01900
02000 [Minsky 1969] M.L.Minsky and S.Papert, "Perceptrons", MIT Press,
02100 Cambridge, Mass, 1969.
02200
02300
02400 [Quam 1972] L. H. Quam, S. Liebes, Jr., R. B. Tucker, M. J. Hannah,
02500 B. G. Eross, "Computer Interactive Picture Processing", Stanford
02600 Artificial Intelligence Laboratory AIM-166, April 1972.
02700
02800
02900 [Winston 1970] Winston, P.H.,"Learning Structural Descriptions from
03000 Examples", AI Technical Report 231, Artificial Intelligence Laboratory,
03100 MIT, September 1970.